Methods, systems and apparatus for automatic video quality assessment

ABSTRACT

Aspects of the present invention are related to systems, methods and apparatus for automatic quality assessment of a video sequence. According to a first aspect of the present invention, a quality index may be generated by combining a spatial quality index and a temporal quality index. According to a second aspect of the present invention, a spatial quality index may be calculated using a modified exponential moving average model to pool multi-scale structural similarity indices computed from test frame—reference frame pairs. According to a third aspect of the present invention, a temporal quality index may be generated by averaging multi-scale structural similarity indices computed from difference image pairs, wherein one difference image is formed between reference frames and another difference image is formed between a reference frame and a test frame.

FIELD OF THE INVENTION

Embodiments of the present invention relate generally to methods,systems and apparatus for automatically assessing the quality of a videosequence and, in particular, for obtaining a quality index for the videosequence.

BACKGROUND

A measurement of the quality of a video sequence may be important in avideo processing system, or other video system. One reliable method forquantifying the quality of a video sequence involves having humansubjects rate the quality of the video sequence. However, this methodmay be time consuming and expensive and, therefore, impractical in someapplications. Methods, systems and apparatus, for automatic videoquality assessment, that determine a quality measure, for a videosequence, that is highly correlated with a human rating may bedesirable.

SUMMARY

Aspects of the present invention are related to systems, methods andapparatus for automatic quality assessment of a video sequence.

According to a first aspect of the present invention, a quality indexmay be generated by calculating a spatial quality index, calculating atemporal quality index and combining the spatial quality index and thetemporal quality index to form a final quality index.

According to a second aspect of the present invention, a spatial qualityindex may be calculated using a modified exponential moving averagemodel to pool multi-scale structural similarity indices computed fromtest frame—reference frame pairs.

According to a third aspect of the present invention, a temporal qualityindex may be generated by averaging multi-scale structural similarityindices computed from difference image pairs, wherein one differenceimage is formed between reference frames and another difference image isformed between a reference frame and a test frame.

The foregoing and other objectives, features, and advantages of theinvention will be more readily understood upon consideration of thefollowing detailed description of the invention taken in conjunctionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL DRAWINGS

FIG. 1 is a chart showing exemplary embodiments of the present inventioncomprising calculating a spatial quality index, calculating a temporalquality index and combining the spatial quality index and the temporalquality index to form a final quality index;

FIG. 2 is a chart showing exemplary embodiments of the present inventioncomprising calculating a plurality of multi-scale structural similarity(MS-SSIM) indices, pooling the indices and selecting the minimum-valuedpooled index as the spatial quality index;

FIG. 3 is a chart showing exemplary embodiments of the present inventioncomprising calculating multi-scale structural similarity (MS-SSIM)indices for a plurality of reference difference frame and reference—testdifference frame pairs and averaging the MS-SSIM index values todetermine a temporal quality index;

FIG. 4 is a picture depicting exemplary embodiments of the presentinvention comprising a spatial-quality-index calculator, atemporal-quality-index calculator and a quality-index combiner forcombining a spatial quality index and a temporal quality index;

FIG. 5 is a picture depicting exemplary embodiments of aspatial-quality-index calculator according to the present invention; and

FIG. 6 is a picture depicting exemplary embodiments of atemporal-quality-index calculator according to the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Embodiments of the present invention will be best understood byreference to the drawings, wherein like parts are designated by likenumerals throughout. The figures listed above are expressly incorporatedas part of this detailed description.

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the methods and systems of the present invention is notintended to limit the scope of the invention, but the detaileddescription is merely representative of the presently preferredembodiments of the invention.

Elements of embodiments of the present invention may be embodied inhardware, firmware and/or a computer program product comprising acomputer-readable storage medium having instructions stored thereon/inwhich may be used to program a computing system. While exemplaryembodiments revealed herein may only describe one of these forms, it isto be understood that one skilled in the art would be able to effectuatethese elements in any of these forms while resting within the scope ofthe present invention.

Although the charts and diagrams in the figures may show a specificorder of execution, it is understood that the order of execution maydiffer from that which is depicted. For example, the order of executionof the blocks may be changed relative to the shown order. Also, as afurther example, two or more blocks shown in succession in a figure maybe executed concurrently, or with partial concurrence. It is understoodby those with ordinary skill in the art that a computer program productcomprising a computer-readable storage medium having instructions storedthereon/in which may be used to program a computing system, hardwareand/or firmware may be created by one of ordinary skill in the art tocarry out the various logical functions described herein.

Some embodiments of the present invention may comprise a computerprogram product comprising a computer-readable storage medium havinginstructions stored thereon/in which may be used to program a computingsystem to perform any of the features and methods described herein.Exemplary computer-readable storage media may include, but are notlimited to, flash memory devices, disk storage media, for example,floppy disks, optical disks, magneto-optical disks, Digital VersatileDiscs (DVDs), Compact Discs (CDs), micro-drives and other disk storagemedia, Read-Only Memory (ROMs), Programmable Read-Only Memory (PROMs),Erasable Programmable Read-Only Memory (EPROMS), Electrically ErasableProgrammable Read-Only Memory (EEPROMs), Random-Access Memory (RAMS),Video Random-Access Memory (VRAMs), Dynamic Random-Access Memory (DRAMs)and any type of media or device suitable for storing instructions and/ordata.

A measurement of the quality of a video sequence may be important in avideo processing system, or other video system. One reliable method forquantifying the quality of a video sequence involves having humansubjects rate the quality of the video sequence. However, this methodmay be time consuming and expensive and, therefore, impractical in someapplications. Methods, systems and apparatus, for automatic videoquality assessment, that determine a quality measure, for a videosequence, that is highly correlated with a human rating may bedesirable.

Some embodiments of the present invention may be described in relationto FIG. 1. FIG. 1 illustrates exemplary method(s) 100 of video qualityassessment according to embodiments of the present invention. In theseembodiments, a test video sequence may be received 102 in a processor.The test video sequence may be, for example, a processed video sequence,a degraded video sequence, a decoded video sequence or any videosequence for which a quality assessment may be desired. The test videosequence may comprise a first plurality of temporally related imageframes, which may be referred to as test frames. A reference videosequence comprising a second plurality of temporally related imageframes, which may be referred to as reference frames, correspondingtemporally to the first plurality of image frames in the test videosequence may be received 104 in the processor. A spatial quality index,also considered a spatial quality measure, for the test video sequence,may be calculated 104, in the processor, using the test video sequenceand the reference video sequence. A temporal quality index, alsoconsidered a temporal quality measure, for the test video sequence, maybe calculated 106, in the processor, using the test video sequence andthe reference video sequence. The spatial quality index and the temporalquality index may be combined 108, in the processor, to form a finalquality index, also considered a final quality measure, for the testvideo sequence. Exemplary processors may include a computationalprocessing system in a computing system, a computational processingsystem in a video processing system, a computational processing systemin a video encoder, a computational processing system in a video decoderand other processors and computational processing units.

The calculation 104 of the spatial quality index, in some embodiments ofthe present invention, may be understood in relation to FIG. 2. FIG. 2illustrates exemplary method(s) 104 of spatial quality index calculationaccording to embodiments of the present invention. In some embodimentsof the present invention, a multi-scale structural similarity (MS-SSIM)index may be calculated 200 for each temporally corresponding test frameand reference frame pair. For each test frame and the temporallycorresponding reference frame, a contrast comparison component and astructure comparison component may be determined for a plurality ofscales, also considered layers. For a particular layer, m, the testframe and the reference frame may be low-pass filtered and down-sampledm−1 times, and the contrast comparison component for the layer, whichmay be denoted c_(m)(x, y), may be computed according to:

${{c_{m}\left( {x,y} \right)} = \frac{{2\sigma_{x,m}\sigma_{y,m}} + C_{2}}{\sigma_{x,m}^{2} + \sigma_{y,m}^{2} + C_{2}}},$and the structure comparison component for the layer, which may bedenoted s_(m)(x, y), may be computed according to:

${{s_{m}\left( {x,y} \right)} = \frac{\sigma_{{xy},m} + C_{3}}{{\sigma_{x,m}\sigma_{y,m}} + C_{3}}},$where x and y may denote aligned image patches in the m^(th)—layer testframe and reference frame, respectively, and σ_(x,m) and σ_(y,m) maydenote the standard deviation of the luminance of x and y, respectively,and σ_(xy,m) may denote the covariance. In some embodiments of thepresent invention, the aligned patches, x and y, may comprise the entiretest frame and reference frame. In alternative embodiments, the alignedpatches, x and y, may comprise a fixed-block-size block in the testframe and in the reference frame. A luminance comparison component,which may be denoted l_(M)(x, y), may be determined only for the highestscale, which may be denoted M, according to:

${{I_{M}\left( {x,y} \right)} = \frac{{2\mu_{x.m}\mu_{y,m}} + C_{1}}{\mu_{x,m}^{2} + \mu_{y,m}^{2} + C_{1}}},$where μ_(x,m) and μ_(y,m) may denote the mean of the luminance of x andy, respectively. The constants C₁, C₂ and C₃ may be stabilizing terms ofthe corresponding components. In an exemplary embodiment of the presentinvention comprising 8 bits-per-pixel luminance images, wherein thedynamic range, which may be denoted L, is equal to 255, the constantsC₁, C₂ and C₃ may be determined according to:

${C_{1} = \left( {K_{1}L} \right)^{2}},{C_{2} = {{\left( {K_{2}L} \right)^{2}\mspace{14mu}{and}\mspace{14mu} C_{3}} = \frac{C_{2}}{2}}},$respectively, where K₁<<1 and K₂<<1. In an exemplary embodiment, K₁=0.01and K₂=0.03. The components may be combined to generate an MS-SSIMindex, for the reference frame—test frame pair, according to:

${{MS}\text{-}{{SSIM}\left( {x,y} \right)}} = {\left\lbrack {l_{M}\left( {x,y} \right)} \right\rbrack^{\alpha_{M}}{\prod\limits_{m = 1}^{M}\;{{\left\lbrack {c_{m}\left( {x,y} \right)} \right\rbrack^{\beta_{m}}\left\lbrack {s_{m}\left( {x,y} \right)} \right\rbrack}^{\gamma_{m}}.}}}$In an exemplary embodiment of the present invention, M=5, α_(M)=0.1333and β_(m=1, . . . , 5)=γ_(m−1, . . . , 5)=[0.0448, 0.2856, 0.3001,0.2363, 0.1333].

The MS-SSIM indices for the reference frame—test frame pairs may bepooled 202 to create a plurality of spatial quality values. In someembodiments of the present invention, the MS-SSIM indices may be pooledusing a modified exponential moving average. An initial spatial qualityvalue, which may be denoted S₁, may be computed according to:

${S_{1} = \frac{\left( {\sum\limits_{i = 1}^{p}{MSSSIM}_{i}} \right)}{p}},$where MSSSIM_(i) denotes the MS-SSIM index of the i^(th) temporallylocated reference frame—test frame pair. For n=1, 2, . . . , N−p, whereN is the number of frames in each the test video sequence and thereference video sequence, S_(n+1) may be computed according to:S _(n+1)=αMSSSIM_(n+p)+(1−α)S _(n),where α is a smoothing factor which may be, in an exemplary embodimentof the present invention, selected according to:

${\alpha = \frac{\eta}{\left( {p + 1} \right)}},$where η=0.25 and p=30. In some embodiments of the present invention,each S_(n) may contain information from, at least, half a second of thevideo, and in each S_(n), a new frame may not make an immediate strongeffect and the contribution of previous frames may not drop too fast. Insome embodiments of the present invention, setting p=30 and α to a smallvalue may achieve the above-described three constraints on S_(n).

In some embodiments of the present invention, the spatial quality of thetest video sequence may be based on the worst-quality video segmentwithin the test video sequence. In these exemplary embodiments, theminimum value of the pooled MS-SSIM indices may be determined 204, andthe spatial quality index, which may be denoted Q_(S), for the testsequence may be set 206 to the minimum value:

$Q_{S} = {\min\limits_{n}{S_{n}.}}$

The calculation 106 of the temporal quality index, in some embodimentsof the present invention, may be understood in relation to FIG. 3. FIG.3 illustrates exemplary method(s) 106 of temporal quality indexcalculation according to embodiments of the present invention. Referencedifference frames, which may be denoted D_(r,i), and reference—testdifference frames, which may be denoted D_(d,i) may be formed 300, 302according to:D _(r,i) =f _(r,i+1) −f _(r,i)andD _(d,i) =f _(d,i+1) −f _(r,i),respectively, where f_(r,i) and f_(r,i+1) may denote temporally adjacentframes within the reference video sequence and f_(d,i+1) may denote thetest frame temporally corresponding to reference frame f_(r,i+1), andwherein i may be a temporal index. An MS-SSIM index may calculated 304for each pair (D_(d,i), D_(r,i)), where i=1, . . . , N−1. The MS-SSIMindex may be calculated according to the method described above. TheMS-SSIM index associated with temporal index i may be denoted T_(i), andthe N−1 MS-SSIM indices may be, in some embodiments of the presentinvention, averaged 306 and the temporal quality index, which may bedenoted Q_(T), may be set 308 to the average index:

$Q_{T} = {\frac{\left( {\sum\limits_{i = 1}^{N - 1}T_{i}} \right)}{N - 1}.}$

In alternative embodiments, the N−1 MS-SSIM indices may be combinedusing a weighted average, an exponential weighting or another datafusion method known in the art.

Referring to FIG. 1, the spatial quality index, Q_(S), and the temporalquality index, Q_(T), may be combined 108 to generate a final qualityindex, which may be denoted Q, for the test video sequence. In someembodiments of the present invention, the spatial quality index, Q_(S),and the temporal quality index, Q_(T), may be combined 108 according to:

$Q = {\frac{\left( {Q_{S} + Q_{T}} \right)}{2}.}$

In alternative embodiments, the spatial quality index, Q_(S), and thetemporal quality index, Q_(T), may be combined using a weighted average,an exponential weighting or another data fusion method known in the art.

The final quality index, Q, may be a value in the range of zero to one,wherein a video sequence with a larger final quality index value maycorrespond to a visibly higher quality video sequence than a videosequence a smaller final quality index value.

Some embodiments of the present invention, described in relation to FIG.4, may comprise a system 400 for computing a quality index for a testvideo sequence. The system 400 may comprise a video-sequence receiver402 for receiving a test video sequence and a reference video sequencecorresponding to the test video sequence. The video-sequence receiver402 may store the test video sequence in a test-sequence memory 404 andthe reference video sequence in a reference-sequence memory 406. Thetest video sequence and the reference video sequence may be madeavailable to a spatial-quality-index calculator 408 and atemporal-quality-index calculator 412 from the test-sequence memory 404and the reference-sequence memory 406, respectively. Thespatial-quality-index calculator 408 may calculate a spatial qualityindex which may be stored in a spatial-quality-index memory 410, and thetemporal-quality-index calculator 412 may calculate a temporal qualityindex which may be stored in a temporal-quality-index memory 414. Thespatial quality index and the temporal quality index may be madeavailable to a quality-index combiner 416 from the spatial-quality-indexmemory 410 and the temporal-quality-index memory 414, respectively. Thequality-index combiner 416 may combine the spatial quality index and thetemporal quality index to form a final quality index which may stored ina final-quality-index memory 418. A final-quality-index transmitter 420may make the final quality index stored in the final-quality-indexmemory 418 available to other processes and/or systems.

The spatial-quality-index calculator 408 may be understood in relationto FIG. 5. FIG. 5 illustrates exemplary embodiments, according to thepresent invention, of the spatial-quality-index calculator 408. Thespatial-quality-index calculator 408 may comprise a controller 500 forcontrolling the processing flow. The spatial-quality-index calculator408 may comprise a video-frame receiver 502 which may be controlled bythe controller 500 to receive a test frame and temporally correspondingreference frame pair. The test frame may be written to a test-framememory 504, and the temporally corresponding reference frame may bewritten to a reference-frame memory 506. The test frame—reference framepair may be made available from the test-frame memory 504 and thereference-frame memory 506 to a multi-scale structural similarity(MS-SSIM)—index calculator 508. The MS-SSIM-index calculator 508 maycalculate an MS-SSIM index for the test frame—reference frame pair, andthe MS-SSIM index may be written to an MS-SSIM-index memory 510, and theMS-SSIM index may be made available from the MS-SSIM-index memory 510 toan MS-SSIM-index pooler 512. The controller 500 may control the dataflow so that each test frame and temporally corresponding referenceframe may be processed, and an MS-SSIM-index calculated for each framepair. When a sufficient number of MS-SSIM indices are available to theMS-SSIM-index pooler 512, a plurality of MS-SSIM indices may be pooled,and the pooled index value may be written to a pooled-index memory 514.The controller may control the initiation of pooling based on the numberof available MS-SSIM indices.

In some embodiments of the present invention, the MS-SSIM indices may bepooled using a modified exponential moving average. An initial spatialquality value, which may be denoted S₁, may be computed according to:

${S_{1} = \frac{\left( {\sum\limits_{i = 1}^{p}{MSSSIM}_{i}} \right)}{p}},$where MSSSIM_(i) denotes the MS-SSIM index of the i^(th) temporallylocated reference frame—test frame pair. For n=1, 2, . . . , N−p, whereN is the number of frames in each the test video sequence and thereference video sequence, S_(n+1) may be computed according to:S _(n+1)=αMSSSIM_(n+p)+(1−α)S _(n),where α is a smoothing factor which may be, in an exemplary embodimentof the present invention, selected according to:

${\alpha = \frac{\eta}{\left( {p + 1} \right)}},$where η=0.25 and p=30. In some embodiments of the present invention,each S_(n) may contain information from, at least, half a second of thevideo, and in each S_(n), a new frame may not make an immediate strongeffect and the contribution of previous frames may not drop too fast. Insome embodiments of the present invention, setting p=30 and α to a smallvalue may achieve the above-described three constraints on S_(n).

A minimum calculator 516 may determine a minimum spatial quality valuefrom the spatial quality values available in the pooled-index memory514, and the minimum spatial quality value may be written to aspatial-quality-index memory 518. A spatial-quality-index transmitter520 may make the spatial quality index stored in thespatial-quality-index memory 518 available to other processes and/orsystems.

The controller 500 may control the data flow and process initiation ofthe components of the spatial-quality-index calculator 408. In someembodiments, the flow may be purely sequential. In alternativeembodiments, the flow may partially concurrent. In yet alternativeembodiments, the flow may substantially concurrent.

The temporal-quality-index calculator 412 may be understood in relationto FIG. 6. FIG. 6 illustrates exemplary embodiments, according to thepresent invention, of the temporal-quality-index calculator 412. Thetemporal-quality-index calculator 412 may comprise a controller 600 forcontrolling the processing flow. The temporal-quality-index calculator412 may comprise a video-frame receiver 602 which may be controlled bythe controller 600 to receive a test frame and temporally correspondingreference frame pair. The test frame may be written to a test-framememory 604, and the temporally corresponding reference frame may bewritten to a reference-frame memory 606. The immediately temporallyprevious reference frame may be received by the video-frame receiver 602and may be written to the reference-frame memory 606. A frame difference608 may form two difference frames according to:D _(r,i) =f _(r,i+1) −f _(r,i)andD _(d,i) =f _(d,i+1) −f _(r,i),where f_(r,i) and f_(r,i+1) may denote the temporally adjacent frameswithin the reference video sequence and f_(d,i+1) may denote the testframe temporally corresponding to reference frame f_(r,i+1) and whereini may be a temporal index. The test frame and the reference frames maybe made available to the frame difference from the test-frame memory 604and the reference-frame memory 606, respectively. An MS-SSIM index maycalculated by an MS-SSIM-index calculator 610 for the frame pair(D_(d,i), D_(r,i)). The MS-SSIM index may be written to an MS-SSIM indexmemory 612. An MS-SSIM-index combiner 614 may combine the MS-SSIMindices for all frame pairs (D_(d,i), D_(r,i)), where i=1, . . . , N−1and N denotes the number of frames in the test video sequence. TheMS-SSIM-index combiner 614 may, in some embodiments of the presentinvention, average the N−1 MS-SSIM indices to form the temporal qualityindex, which may be denoted Q_(T), according to:

${Q_{T} = \frac{\left( {\sum\limits_{i = 1}^{N - 1}T_{i}} \right)}{N - 1}},$where T_(i) may denote the MS-SSIM index associated with the frame pair(D_(d,i), D_(r,i)).

In alternative embodiments, the N−1 MS-SSIM indices may be combinedusing a weighted average, an exponential weighting or another datafusion method known in the art.

The temporal quality index may be written to a temporal-quality-indexmemory 618 and may be made available to other processes and/or systemsby a temporal-quality-index transmitter 620.

The controller 600 may control the data flow and process initiation ofthe components of the temporal-quality-index calculator 412. In someembodiments, the flow may be purely sequential. In alternativeembodiments, the flow may partially concurrent. In yet alternativeembodiments, the flow may substantially concurrent.

Referring to FIG. 4, in some embodiments of the present invention, thequality-index combiner 416 may combine the spatial quality index, whichmay be denoted Q_(S), and the temporal quality index, which may bedenoted Q_(T), to generate the final quality index, which may be denotedQ, for the test video sequence, according to:

$Q = {\frac{\left( {Q_{S} + Q_{T}} \right)}{2}.}$

In alternative embodiments, the spatial quality index, Q_(S), and thetemporal quality index, Q_(T), may be combined in the quality-indexcombiner 416 using a weighted average, an exponential weighting oranother data fusion method known in the art.

The final quality index, Q, may be a value in the range of zero to one,wherein a video sequence with a larger final quality index value maycorrespond to a visibly higher quality video sequence than a videosequence a smaller final quality index value.

Some embodiments of the present invention may comprise a videoprocessing apparatus in which the above described methods and/or systemsmay be embodied. Exemplary video processing apparatus may be video testdevices, video encoders, video decoders and other apparatus in which ameasurement of video quality may be required.

The terms and expressions which have been employed in the foregoingspecification are used therein as terms of description and not oflimitation, and there is no intention in the use of such terms andexpressions of excluding equivalence of the features shown and describedor portions thereof, it being recognized that the scope of the inventionis defined and limited only by the claims which follow.

What is claimed is:
 1. A method for determining a quality index for atest video sequence, said method comprising: receiving, in a processor,a test video sequence, wherein said test video sequence comprises afirst plurality of image values; receiving, in said processor, areference video sequence corresponding to said test video sequence,wherein said reference video sequence comprises a second plurality ofimage frames; in said processor, calculating a spatial quality indexusing said test video sequence and said reference video sequence,wherein said calculating a spatial quality index comprises: calculatinga multi-scale structural similarity (MS-SSIM) index for each image framein said first plurality of image frames and a temporally correspondingimage frame in said second plurality of image frames, thereby producinga plurality of MS-SSIM indices; pooling said plurality of MS-SSIMindices, thereby producing a plurality of pooled MS-SSIM indices;determining a minimum value from said plurality of pooled MS-SSIMindices: and setting said spatial quality index to said minimum value;in said processor, calculating a temporal quality index using said testvideo sequence and said reference video sequence; and in said processor,combining said spatial quality index and said temporal quality index toform a final quality index for said test video sequence.
 2. A method asdescribed in claim 1, wherein said test video sequence is a degradedversion of said reference video sequence, a processed version of saidreference video sequence or a previously compressed version of saidreference video sequence.
 3. A method as described in claim 1, whereinsaid combining comprises averaging said spatial quality index and saidtemporal quality index.
 4. A method as described in claim 1, whereinsaid pooling said plurality of MS-SSIM indices comprises: calculating aninitial pooled MS-SSIM index by averaging a first plurality of saidMS-SSIM indices in said plurality of MS-SSIM indices, wherein said firstplurality of said MS-SSIM indices corresponds to a temporally initialportion of said MS-SSIM indices in said plurality of MS-SSIM indices;calculating a first subsequent pooled MS-SSIM index by forming a linearcombination of said initial pooled MS-SSIM index and a first nextMS-SSIM index, wherein said first next MS-SSIM index is an immediatelytemporally subsequent MS-SSIM index to said temporally initial portionof said MS-SSIM indices in said plurality of MS-SSIM indices; andcalculating a second subsequent pooled MS-SSIM index by forming a linearcombination of said first subsequent pooled MS-SSIM index and a secondnext MS-SSIM index, wherein said second next MS-SSIM index is animmediately temporally subsequent MS-SSIM index to said first nextMS-SSIM index in said plurality of MS-SSIM indices.
 5. A method asdescribed in claim 4, wherein said initial portion of said MS-SSIMindices is associated with a portion of video at least one-half secondin length.
 6. A method as described in claim 4, wherein: saidcalculating a temporal quality index comprises: forming a firstreference difference image between a first image frame in said secondplurality of image frames and a second image frame in said secondplurality of image frames, wherein said second image frame is animmediately temporally previous image frame to said first image frame insaid second plurality of image frames; forming a first test differenceimage between a test image frame in said first plurality of imageframes, wherein said test image frame corresponds temporally to saidfirst image frame, and said second image frame; calculating adifference-frames multi-scale structural similarity (MS-SSIM) indexusing said first reference difference image and said first testdifference image; and averaging said difference-frames MS-SSIM indexwith a plurality of previously calculated difference-frames MS-SSIMindices.
 7. A method as described in claim 6, wherein said combiningcomprises averaging said spatial quality index and said temporal qualityindex.
 8. A method as described in claim 1, wherein: said calculating atemporal quality index comprises: forming a first reference differenceimage between a first image frame in said second plurality of imageframes and a second image frame in said second plurality of imageframes, wherein said second image frame is an immediately temporallyprevious image frame to said first image frame in said second pluralityof image frames; forming a first test difference image between a testimage frame in said first plurality of image frames, wherein said testimage frame corresponds temporally to said first image frame, and saidsecond image frame; calculating a difference-frames multi-scalestructural similarity (MS-SSIM) index using said first referencedifference image and said first test difference image; and averagingsaid difference-frames MS-SSIM index with a plurality of previouslycalculated difference-frames MS-SSIM indices.
 9. A method fordetermining a quality index for a test video sequence, said methodcomprising: receiving, in a processor, a test video sequence, whereinsaid test video sequence comprises a first plurality of image frames;receiving, in said processor, a reference video sequence correspondingto said test video sequence, wherein said reference video sequencecomprises a second plurality of image frames; in said processor,calculating a spatial quality index using said test video sequence andsaid reference video sequence; in said processor, calculating a temporalquality index using said test video sequence and said reference videosequence, wherein and said calculating a temporal quality indexcomprises: forming a first reference difference image between a firstimage frame in said second plurality of image frames and a second imageframe in said second plurality of image frames, wherein said secondimage frame is an immediately temporally previous image frame to saidfirst image frame in said second plurality of image frames; forming afirst test difference image between a test image frame in said firstplurality of image frames, wherein said test image frame correspondstemporally to said first image frame, and said second image frame;calculating a multi-scale structural similarity (MS-SSIM) index usingsaid first reference difference image and said first test differenceimage; and averaging said MS-SSIM index with a plurality of previouslycalculated MS-SSIM indices. and in said processor, combining saidspatial quality index and said temporal quality index to form a finalquality index for said test video sequence.
 10. A method for determininga quality index for a test video sequence, said method comprising:receiving, in a processor, a test video sequence, wherein said testvideo sequence comprises a first plurality of image frames; receiving,in said processor, a reference video sequence corresponding to said testvideo sequence, wherein said reference video sequence comprises a secondplurality of image frames; and in said processor, calculating a spatialquality index using said test video sequence and said reference videosequence, wherein said calculating comprises: calculating a multi-scalestructural similarity (MS-SSIM) index for each image frame in said firstplurality of image frames and a temporally corresponding image frame insaid second plurality of image frames, thereby producing a plurality ofMS-SSIM indices; pooling said plurality of MS-SSIM indices, therebyproducing a plurality of pooled MS-SSIM indices; determining a minimumvalue from said plurality of pooled MS-SSIM indices; and setting saidspatial quality index to said minimum value.
 11. A method as describedin claim 10, wherein said pooling said plurality of MS-SSIM indicescomprises: calculating an initial pooled MS-SSIM index by averaging afirst plurality of said MS-SSIM indices in said plurality of MS-SSIMindices, wherein said first plurality of said MS-SSIM indicescorresponds to a temporally initial portion of said MS-SSIM indices insaid plurality of MS-SSIM indices; calculating a first subsequent pooledMS-SSIM index by forming a linear combination of said initial pooledMS-SSIM index and a first next MS-SSIM index, wherein said first nextMS-SSIM index is an immediately temporally subsequent MS-SSIM index tosaid temporally initial portion of said MS-SSIM indices in saidplurality of MS-SSIM indices; and calculating a second subsequent pooledMS-SSIM index by forming a linear combination of said first subsequentpooled MS-SSIM index and a second next MS-SSIM index, wherein saidsecond next MS-SSIM index is an immediately temporally subsequentMS-SSIM index to said first next MS-SSIM index in said plurality ofMS-SSIM indices.
 12. A method as described in claim 11, wherein saidinitial portion of said MS-SSIM indices is associated with a portion ofvideo at least one-half second in length.
 13. A method as described inclaim 10 further comprising combining said spatial quality index with atemporal quality index.
 14. A method as described in claim 13, whereinsaid combining comprises averaging said spatial quality index and saidtemporal quality index.
 15. A method as described in claim 10, whereinsaid test video sequence is a degraded version of said reference videosequence, a processed version of said reference video sequence or apreviously compressed version of said reference video sequence.
 16. Amethod for determining a quality index for a test video sequence, saidmethod comprising: receiving, in a processor, a test video sequence,wherein said test video sequence comprises a first plurality of imageframes; receiving, in said processor, a reference video sequencecorresponding to said test video sequence, wherein said reference videosequence comprises a second plurality of image frames; and in saidprocessor, calculating a temporal quality index using said test videosequence and said reference video sequence, wherein said calculatingcomprises; forming a first reference difference image between a firstimage frame in said second plurality of image frames and a second imageframe in said second plurality of image frames, wherein said secondimage frame is an immediately temporally previous image frame to saidfirst image frame in said second plurality of image frames; forming afirst test difference image between a test image frame in said firstplurality of image frames, wherein said test image frame correspondstemporally to said first image frame, and said second image frame;calculating a multi-scale structural similarity (MS-SSIM) index usingsaid first reference difference image and said first test differenceimage; and averaging said MS-SSIM index with a plurality of previouslycalculated MS-SSIM indices.
 17. A method as described in claim 16further comprising combining said temporal quality index with a spatialquality index.
 18. A method as described in claim 17, wherein saidcombining comprises averaging said spatial quality index and saidtemporal quality index.
 19. A method as described in claim 17, whereinsaid test video sequence is a degraded version of said reference videosequence, a processed version of said reference video sequence or apreviously compressed version of said reference video sequence.